Create your graph data model

Step 4: Create your data model

The data model defines how your CSV data becomes a graph. In the Movies dataset example, you will create: * Movie nodes - Each movie becomes a node with properties like title and movieId * Person nodes - Each actor becomes a node with properties like name and personId * ACTED_IN relationships - These connections link actors to movies, enabling queries that traverse relationships

How to do it: Click Create model manually to start building your graph structure.

Understanding nodes and relationships in context

How this relates to Neo4j Fundamentals:

If you’ve taken the Neo4j Fundamentals course, you learned that graphs consist of: * Nodes (vertices) - Entities in your domain (like movies and actors) * Relationships (edges) - Connections between entities (like ACTED_IN) * Properties - Attributes stored on nodes and relationships (like title on Movie nodes, characters on ACTED_IN relationships) * Labels - Categories for nodes (like Movie and Person)

In this import, you’re creating these graph elements from your CSV data: * Each unique movie becomes a Movie node with a title property * Each unique actor becomes a Person node with a name property * Each actor-movie pair becomes an ACTED_IN relationship with a characters property

How this relates to Graph Data Modeling Fundamentals:

If you’ve taken the Graph Data Modeling Fundamentals course, you learned about: * Instance models - The actual nodes and relationships in your graph (what you’re creating now) * Domain models - The conceptual design of your graph (Movie and Person connected by ACTED_IN)

The Data Importer helps you create an instance model from your CSV. You’re deciding: * Which entities become nodes (Movie, Person) * Which connections become relationships (ACTED_IN) * Which CSV columns become properties (title, name, characters)

This modeling step is critical—a well-designed model makes recommendation queries fast and intuitive.

How this relates to Importing Data Fundamentals:

If you’ve taken the Importing Data Fundamentals course, you learned about: * Import methods - Different ways to load data (Data Importer, LOAD CSV, neo4j-admin import) * Data transformation - Converting tabular data (CSV) into graph structures (nodes and relationships) * Unique constraints - Ensuring nodes aren’t duplicated (using movieId and personId as unique identifiers)

In this lesson, you’re using the Data Importer (a visual, no-code tool) to transform your CSV into a graph. The Data Importer automatically handles: * Creating unique nodes (using movieId and personId as keys) * Mapping CSV columns to node and relationship properties * Generating efficient Cypher statements for the import

Key takeaway: Nodes and relationships are the building blocks you learned about in Neo4j Fundamentals. The modeling principles from Modeling Fundamentals guide how you structure them. The import techniques from Importing Fundamentals show you how to create them from your data. This lesson combines all three—you’re applying fundamentals to create a graph data model.

Step 5: Define Movie nodes

In the Movies dataset example, Movie nodes represent the main entities. Each Movie node has properties (title, movieId) that you can use in queries.

How to do it:

How it works in the background: When you map movieId and title, the Data Importer will create Cypher statements like:

CREATE (m:Movie {movieId: '123', title: 'The Matrix'})

This creates Movie nodes that your recommendation queries can traverse.

After adding the label, you can edit the model structure to refine how your CSV data maps to graph elements.

Step 6: Define Person nodes

In the Movies dataset example, Person nodes represent actors. When you query "Find movies with Tom Hanks," you’re traversing from a Person node through ACTED_IN relationships to Movie nodes.

How to do it:

Example for recommendations: Once imported, you’ll be able to query:

MATCH (p:Person {name: 'Tom Hanks'})-[:ACTED_IN]->(m:Movie)
RETURN m.title

This finds all movies Tom Hanks acted in—the foundation of actor-based recommendations.

Optional: Edit property types by clicking the pencil icon next to each property. For example, you might want to ensure personId is stored as an integer for better query performance.

Step 7: Define ACTED_IN relationships

Relationships connect nodes in your graph. The ACTED_IN relationship connects Person nodes to Movie nodes, enabling queries like: * "Find all movies with the same actors" (traverse from Movie through ACTED_IN to Person, then back to other Movies) * "Find actors who worked together" (find two Person nodes connected to the same Movie)

How to do it:

How it works in the background: The Data Importer creates Cypher statements like:

MATCH (p:Person {personId: '123'}), (m:Movie {movieId: '456'})
CREATE (p)-[:ACTED_IN {characters: ['Neo']}]->(m)

This creates the connections that enable relationship traversal in your graph.

Verification: The green checkmark indicates that the relationship mapping is correct. Your model now shows Person nodes connected to Movie nodes via ACTED_IN relationships—exactly what you need for recommendation queries.

Step 8: Review and confirm your model

Before importing, verify that your model correctly maps CSV data to graph structure. Incorrect mappings mean your queries won’t work.

How to do it:

How it works in the background: The Data Importer analyzes your CSV to ensure: * No duplicate nodes (uses movieId/personId as unique keys) * All relationships can be created (both Person and Movie nodes exist) * Data types are correct (strings, numbers, etc.)